Natural language is
often indicative of one's emotion. Hence, emotion recognition has
been enjoying popularity in the field of NLP [1]}, [2]}, due to its widespread applications in
opinion mining, recommender systems, healthcare, and so on. Only in the past few years has emotion recognition in conversation (ERC) gained attention from the NLP community [3]}, [4]}, [5]}, [6]}
due to the growing availability of public conversational data. ERC can be used to analyze
conversations that take place on social media. It can also aid in analyzing conversations in real time, which can be instrumental in legal trials, interviews, e-health services, and more. Unlike vanilla emotion recognition of sentences/utterances, ERC ideally requires context
modeling of the individual utterances. This context can be attributed to the preceding
utterances, and relies on the temporal sequence of utterances.
Compared to the recently published works on ERC [4]}, [5]}, [6]}, [10]}, [11]}, [12]}, both lexicon-based [13]}, [14]}, [15]} and modern deep learning-based [1]}, [2]} vanilla emotion recognition approaches fail to work well on ERC datasets as this work ignores the conversation specific factors such as the presence of contextual cues, the temporality in speakers' turns, or speaker-specific information.
